Master of Data Science (Applied) [Online] (MDS(App))
Master of Data Science (Applied) [Online] (MDS(App))
Program Code
MDSA
Program Faculty
Faculty of Sciences, Engineering and Technology
Academic Year
2025
These Program Rules should be read in conjunction with the University's policies (https://www.adelaide.edu.au/policies).
Overview
The Master of Data Science (Applied) [Online] is a fully online program suitable for students from a wide range of backgrounds and will provide the opportunity to build and consolidate practical and research skills in the application of data science. Data Science is a rapidly evolving space with implications for the future of work and our daily lives.
Students will undertake a fixed study plan within the online program, moving through four course carousels that structure their learning to build on appropriate prerequisite knowledge. The first carousel (Graduate Certificate) provides foundational content in data science, including fundamental technical content, as well as an overview of data science as an organisational process. The second carousel (Graduate Diploma), then extends this content further, providing deeper technical awareness of data science, as well as exploration of case studies of application, communication and visualisation from multiple disciplines. The third carousel (Master level) will extend the necessary skills to critically analyse data and its impact to drive changes in their organisation or area of expertise. The applied research project in the fourth carousel is where learners demonstrate their mastery through research.
The Master of Data Science (Applied) [Online] is an AQF Level 9 qualification with a standard full-time duration of 2 years. However, this program is only offered part-time for a duration of 2.7 years.
Program Learning Outcomes
- Apply, evaluate and use the principles of data science within real-world contexts, including the specific requirements of large-scale data analysis, in an area of specialisation.
- Describe, synthesise and analyse the technical practice, management and strategic impact of data science, and its application, within industry contexts.
- Apply, evaluate and use best-practice tools, techniques and theory of data science within a range of application domains.
- Adopt and employ professional attitudes, ethics, codes of conduct, standards and values.
- Use highly effective interpersonal skills to enable empathetic and effective communication with a range of audiences.
- Synthesise, apply, and evaluate new data analysis workflows in the context of an applied research project.
Conditions
Interruption of program: students must apply for permission from the Faculty before taking a Leave of Absence. Any extension of the leave without approval will result in the loss of place in the program but an application may be made to be re-admitted to the program subject to the admission procedures in place at the time.
Program Maximum Duration: As specified in Table 1 of the Coursework Academic Programs Policy, this program must be completed within a Maximum Duration which includes any periods of non-enrolment, leave of absence or approved study at other institutions for credit towards a Program.
Academic Program Rules for Master of Data Science (Applied) [Online]
There shall be a Master of Data Science (Applied) [Online].
Qualification Requirements
To qualify for the degree of Master of Data Science (Applied) [Online], the student must complete satisfactorily a program of study consisting of the following requirements with a combined total of not less than 48 units, comprising:
- Core courses to the value of 48 units
- Students entering from a cognate discipline, and therefore restricted from undertaking COMP SCI 7210OL Foundations of Computer Science – Python A and/or COMP SCI 7211OL Foundations of Computer Science – Python B will be required to undertake the alternative course offerings. Students with permission to undertake alternative course offerings can contact Student Success Team for further information.
- Students without Stage 2 Mathematical Methods (or equivalent) are required to take MATHS 7203OL Applied Data Science and Mathematics in lieu of DATA 7202OL Applied Data Science. Students with permission to undertake alternative course offerings can contact Student Success Team for further information.
Core Courses
-
Core Courses
To satisfy the requirements for Core Courses students must complete courses to the value of 48 units.
All of the following courses must be completed:
Subject/Catalogue Course Title Unit Value COMP SCI 7210OL Foundations of Computer Science - Python A 3 COMP SCI 7211OL Foundations of Computer Science - Python B 3 or
Students entering from a cognate discipline, and therefore restricted from undertaking COMP SCI 7210OL Foundations of Computer Science – Python A and/or COMP SCI 7211OL Foundations of Computer Science – Python B will be required to undertake the alternative course offerings.
and
Courses to the value of 3 units from the following:
Subject/Catalogue Course Title Unit Value DATA 7202OL Applied Data Science 3 MATHS 7203OL Applied Data Science and Mathematics 3 and
All of the following courses must be completed:
Subject/Catalogue Course Title Unit Value APP MTH 7201OL Decision Sciences 3 COMMGMT 7023OL Business Data & Cyber Security (M) 3 COMP SCI 7212OL Human and Ethical Factors in Computer Science 3 COMP SCI 7317OL Using Machine Learning Tools PG 3 COMP SCI 7319OL Big Data Analysis & Industry Project 3 COMP SCI 7415OL Research Methods 3 DATA 7201OL Data Taming, Modelling and Visualisation 3 DATA 7203OL Working with Big Data 3 DATA 7301OL Applications of Data Science 3 DATA 7302OL Real Data: Modern Methods for Finding Hidden Patterns 3 MATHS 7027OL Mathematical Foundations of Data Science 3 and
All of the following courses must be completed:
The following two courses must be completed in consecutive online teaching periods. Students are required to take both Part A and the matching Part B course.
Subject/Catalogue Course Title Unit Value DATA 7303AOL Data Science Research Project A 0 DATA 7303BOL Data Science Research Project B 6